Abstract : This paper proposes a feature extraction method for
online handwritten characters for a penmanship learning support system.
This system has a database of model characters. It evaluates the
characters a learner writes by comparing them with the model characters.
However, if we prepare feature information for every character,
information must be input every time a model character is added.
Therefore, we propose a method of automatically extracting features from
handwritten characters. In this paper, we examine whether it correctly
identifies the turns in strokes as features. The resulting extraction
rate is 80% and in the remaining 20% of cases, it extracted an area near
a turn.